A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | AB | AC | AD | ||
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1 | SSN UCs / Requirements | Sensor metadata: It should be possible to include metadata about the sensors producing the observations. | CRS definition: The URI of the coordinate reference system (CRS) shall be specified when geographic coordinates are present in the data. | Georeferenced sensor data: It should possible to georeference observations. | Positioning system: It should be possible to define the positioning system used to determine the spatial location in the data. | Model reuse: Spatial data modelling issues solved in existing observation models shall be considered for adoption, e.g. O&M. | Reference external vocabularies: It should be possible to refer to externally-managed controlled vocabularies. | Observation aggregations: It should be possible to represent aggregations of observations. | Mobile sensors: It should be possible to represent sensors that change their location, as well as the current location of the sensor at the observation time. | Current location: It should be possible to get/represent the current location of the sensor providing the observation". | Moving features: It should be possible to refer to features that change their location. | Linkability: It should be possible to link to other data on the Web. | Nominal observations: It should be possible to represent qualitative and nominal observations. | Space-time multi-scale: It should be possible to represent and integrate data over spatial and temporal scales. | Provenance: It should be possible to add provenance metadata. | Multilingual support: It should be possible to add metadata in different languages. | dependency between data and actuation decision (out of scope?) | Humans as sensors: It should be possible to represent observations taken by human individuals or communities acting as sensors perceiving the environment. | Lightweight API: A lightweight API is needed for implementation on IoT devices. | Time series: It must be possible to represent time series of sensor data (see also coverage) | Spatial vagueness: It should be possible to describe locations in a vague, imprecise manner. For instance, to represent spatial descriptions from crowdsourced observations, such as "we saw a wildfire at the bottom of the hillside" or "we felt a light tremor while walking by Los Angeles downtown". Another related use cases deal with spatial locations identified in historical texts, e.g. a battle occurred at the south west boundary of the Roman Empire. | 3D support: It must be possible to represent locations of sensors and observations in 3D space (included in Best Practice reqs, column P). | Virtual observations: It must be possible to represent synthetic observations made by computational procedures or inference. | Ex-situ sampling: It should be possible to represent ex-situ (remote) sampling or sensing (as opposite to in-situ; defined in UC32) | Sampling topology: It should be possible to represent topological relationships between observation samples, e.g. specimens located along a borehole or probe spots found on a polished section of rocks. | Uncertainty in observations: It must be possible to represent uncertainty in observations. | Sensing procedure: It should be possible to attach the procedural description of a sensing method. | Profiling, e.g. for checking compliance to standard model. | Models as sensors | Dynamic sensor data: It should be possible to represent near real-time streaming sensor measurements. | |
2 | 2. Meteorological Data Rescue Use Case | quality metadata | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | |||||||||||||||||||
3 | 3. Habitat zone verification for designation of Marine Conservation Zones | yes | yes | e.g. video | yes | yes | yes | yes | ala O&M. Fine-grained | yes | yes | yes | yes | ||||||||||||||||||
4 | 4. Real-time Wildfire Monitoring | yes | yes | yes | link to social media, processes and other sources | yes | |||||||||||||||||||||||||
5 | 5. Diachronic Burnt Scar Mapping | yes | yes | yes | link to social media | ||||||||||||||||||||||||||
6 | 12. Integration of governmental and utility data to enable smart grids | yes | yes | ||||||||||||||||||||||||||||
7 | 14. Publication of air quality data aggregations | yes | yes | yes | |||||||||||||||||||||||||||
8 | 15. Publication of transport card validation and recharging data | yes | |||||||||||||||||||||||||||||
9 | 18. Various Sensor Use Cases | ||||||||||||||||||||||||||||||
10 | 21. Publication of Raw Subsurface Monitoring Data | yes | yes | yes | yes | yes | yes | yes, e.g. by time unit or depth unit. | yes | yes, e.g. qualitative values for lithology. | yes, e.g. public perception of ground tremors. | yes | yes | yes | yes, e.g. a seismic event is calculated as originating from a subsurface location following observations at various surface sensors. | yes | yes, and uncertainty can include upper and lower limits of detectability/recording, though this could be detailed in the procedural description. | yes | yes | ||||||||||||
11 | 22. Use of a place name ontology for geo-parsing text and geo-enabling searches | yes | |||||||||||||||||||||||||||||
12 | 23. Driving to work in the snow | discovery | link observation to features in space and time. ssn:featureOfInterest? | yes | yes | yes | yes | user behaviour | yes | yes | |||||||||||||||||||||
13 | 24. Intelligent Transportation System | yes | yes | yes | yes | yes | |||||||||||||||||||||||||
14 | 25. Optimizing energy consumption, production, sales and purchases in Smart Grids | yes | |||||||||||||||||||||||||||||
15 | 26 "101" Smart City Use-cases | yes | yes, how to update frequently? | yes | yes | yes | yes | ||||||||||||||||||||||||
16 | 28. Images, e.g. a Time series of a Water Course | yes | yes | e.g. photos | yes | yes | yes | yes | yes | timeseries of images | yes | ||||||||||||||||||||
17 | 29. Droughts in geological complex environments where groundwater is important | yes | yes | yes | yes | yes | yes | yes | |||||||||||||||||||||||
18 | 30. Soil data applications | yes, e.g. O&M. | yes | yes | yes | yes | yes | yes | |||||||||||||||||||||||
19 | 31. Bushfire response coordination centre | yes - related to place | yes | yes | yes | yes | |||||||||||||||||||||||||
20 | 32. Observations on geological samples | yes | yes | yes; specimen model provided in O&M. Also consider PROV-O. | yes | yes | yes, in this case we are looking at provenance of real world things. | yes | yes | yes | yes | ||||||||||||||||||||
21 | 33. Spatial Sampling | yes | yes | yes | yes | yes, e.g. O&M. | yes | yes, e.g. representations of real world features. | yes | yes | yes | yes | |||||||||||||||||||
22 | 35. Satellite data processing | yes | yes | yes | yes; satellite position. | yes | yes | yes | yes | yes | yes | yes; validation of data completeness. | |||||||||||||||||||
23 | 36. Marine observations - eMII | yes | yes | yes | yes | yes | yes | ||||||||||||||||||||||||
24 | 37. Marine observations - data providers | yes | yes | yes; qualitative observations. | yes | yes | |||||||||||||||||||||||||
25 | 38. Marine observations - data consumers | yes | |||||||||||||||||||||||||||||
26 | 41. Metadata and Search Granularity | yes | yes | yes | |||||||||||||||||||||||||||
27 | 42. Crowdsourced earthquake observation information | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | |||||||||||||||||
28 | 44. Crop Yield Estimation using multiple satellites | yes | yes | yes | yes | yes | yes | yes | yes | yes | |||||||||||||||||||||
29 | 49. Creation of “virtual observations” from “analysis” phase of weather prediction model | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | |||||||||||||||||||
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